Absolute Value Principal Components Analysis (AVPCA) and Parameter Estimation (PE) to bearing fault detection using rotor speed signal monitoring — A comparative study

In this paper, a comparative experimental study between the Parameter Estimation (PE) technique and the Absolute Value Principal Component Analysis (AVPCA) algorithm to bearing fault detection using rotor speed signal monitoring is represented. The PE technique relies on the residuals between the input/output (Voltage/Speed) signals of the real system and of the estimated model. AVPCA, in other hand base on the Sum Square Error (SSE) distance between the training-databases and the tested-databases from just only the output signal (Speed) and its minimum. The experimental results reveal that the AVPCA algorithm is more effective in detecting bearing faults than the PE technique using rotor speed signal monitoring.

[1]  M. J. Fuente,et al.  Fault detection and isolation in transient states using principal component analysis , 2012 .

[2]  Bertrand Raison,et al.  Models for bearing damage detection in induction motors using stator current monitoring , 2008, 2004 IEEE International Symposium on Industrial Electronics.

[3]  José Ragot,et al.  Sensor Failure Detection of Air Quality Monitoring Network , 2000 .

[4]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[5]  Dongik Lee,et al.  Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA) , 2014, 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014).

[6]  Kamel Benothman,et al.  Determination of principal component analysis models for sensor fault detection and isolation , 2013 .

[7]  Dongik Lee,et al.  Fault Diagnosis of Electronic Throttle System Using Parameter Estimation , 2011 .

[8]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[9]  Rolf Isermann,et al.  Application of model-based fault detection to a brushless DC motor , 2000, IEEE Trans. Ind. Electron..

[10]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

[11]  Xun Wang,et al.  Nonlinear PCA With the Local Approach for Diesel Engine Fault Detection and Diagnosis , 2008, IEEE Transactions on Control Systems Technology.

[12]  Kalyana Chakravarthy Veluvolu,et al.  Rotor Speed-Based Bearing Fault Diagnosis (RSB-BFD) Under Variable Speed and Constant Load , 2015, IEEE Transactions on Industrial Electronics.

[13]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..